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Forty paired longitudinal PET/CT and CT datasets were grouped into four cancer cohorts and by burden (low, intermediate, high). Two nuclear medicine physicians independently matched lesions; reader agreement consensus served as gold standard. Results. Automated performance did not differ significantly from IRV for any metric in any cohort (p > 0.05). Automated matching was significantly more efficient (p \u003C 0.001) while meeting IRV benchmarks. Conclusion. Automated lesion matching expedites analysis and improves longitudinal 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was the objective of the study?","Question",{"text":63,"@type":64},"To assess how well an automated lesion-matching algorithm performs compared with inter-reader variability when matching longitudinal lesions between scans in metastatic cancer patients.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How were the scans and cohorts organized?",{"text":68,"@type":64},"Forty paired longitudinal PET/CT and CT scan pairs were grouped into four cancer cohorts and further stratified by cancer burden into low, intermediate, and high groups.",{"name":70,"@type":61,"acceptedAnswer":71},"What did the results show about accuracy and efficiency?",{"text":72,"@type":64},"Automated matching did not differ significantly from inter-reader variability across metrics in any cohort, and it was significantly more efficient than manual reader 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 \nPAPER   Performance of an automated registration-based method for OPENACCESS longitudinal lesion matching and comparison to inter-reader  \nR5ECEIVMayE023 variability  \nREVISED  \n25July2023 Daniel T Huff1,∗ , Victor Santoro-Fernandes2 , Song Chen3, Meijie Chen3, Carl Kashuk1,  \nCEPTAuEgD FORust20U3BLICATION Amy J Weisman 1 , Robert Jeraj2,4  and Timothy G Perk1   \n1 AIQ Solutions, Madison, WI, United States ofAmerica  \nPUBLISHED  \n28August2023 2 University ofWisconsin-Madison, Department ofMedical Physics, Madison, WI, United States ofAmerica  \n3 The First Hospital ofChina Medical University, Department ofNuclear Medicine, Shenyang, Liaoning, CN, People’s Republic ofChina  \n~~ ~~ 4 University ofLjubljana, Faculty ofMathematics and Physics, Ljubljana, SI, Slovenia Origworkinalcmayobnetentusefdromthunderis ∗ Author to whomany correspondence should be addressed.  \nthe terms ofthe Creative [E-mail:](E-mail: daniel.huff@aiq-solutions.com)[ daniel.huff@aiq-solutions.com](E-mail: daniel.huff@aiq-solutions.com)  \nCommonsAttribution4.0  \nlicence. Keywords: PET/CT, longitudinal, inter-reader variability, reproducibility  \nAny further distribution of this work must maintain attribution to the  \nauthor(s) and the title of the work, journal citation and DOI.  \nAbstract  \nObjective. Patients with metastatic disease are followed throughout treatment with medical imaging, and accurately assessing changes ofindividual lesions is critical to properly inform clinical decisions. The goal ofthis workwas to assess the performance ofan automated lesion-matching algorithm in comparison to inter-reader variability (IRV) ofmatching lesions between scans ofmetastatic cancer patients. Approach. Forty pairs oflongitudinal PET/CT and CT scans were collected and organized into four cohorts: lung cancers, head and neck cancers, lymphomas, and advanced cancers. Cases were also divided by cancer burden: low-burden ( \u003C 10lesions), intermediate-burden (10–29), and highburden (30+). Two nuclear medicine physicians conducted independent reviews ofeach scan-pair and manually matched lesions. Matching differences between readers were assessed to quantifythe IRV oflesion matching. The two readers met to forma consensus, which was considered a gold standard and compared against the output ofan automated lesion-matching algorithm. IRVand performance ofthe automated method were quantified using precision, recall, F1-score, and the number ofdifferences. Main results. The performance ofthe automated method did not differ significantly from IRV for any metric in any cohort (p > 0.05, Wilcoxon paired test). In high-burden cases, the F1-score (median [range])was0.89 [0.63,1.00]between the automated method and reader consensus and0 .93 [0 .72,1 .00]between readers. In low-burden cases, F1-scores were1 .00 [0 .40,1 .00] and1.00 [0.40,1.00], for the automated method and IRV, respectively. Automated matching was significantly more efficient than either reader (p \u003C 0.001). In high-burden cases, median matching time for the readers was60and30 min, respectively, while automated matching took a median of 3.9 min Significance. The automated lesion-matching algorithm was successful in performing lesion matching, meeting the benchmark ofIRV. Automated lesion matching can significantly expedite and improve the consistency oflongitudinal lesion-matching.  \n1. Introduction  \nPatients with metastatic cancers are often imaged longitudinally throughout the course oftheir disease for diagnosis, staging, and response assessment with a variety ofmodalities including comp","cbCaie51R5C2zjBV","https://ap.wps.com/l/cbCaie51R5C2zjBV","pdf",859581,12,"English","# Abstract\n# Introduction","[{\"question\":\"What was the objective of the study?\",\"answer\":\"To assess how well an automated lesion-matching algorithm performs compared with inter-reader variability when matching longitudinal lesions between scans in metastatic cancer patients.\"},{\"question\":\"How were the scans and cohorts organized?\",\"answer\":\"Forty paired longitudinal PET/CT and CT scan pairs were grouped into four cancer cohorts and further stratified by cancer burden into low, intermediate, and high groups.\"},{\"question\":\"What did the results show about accuracy and efficiency?\",\"answer\":\"Automated matching did not differ significantly from inter-reader variability across metrics in any cohort, and it was significantly more efficient than manual reader matching.\"}]","Performance of an automated registration-based method for OPENACCESS longitudinal lesion matching and comparison to inter-reader variability - Abstract | PDF",1790252153]